Sequential greedy approximation for certain convex optimization problems

A greedy algorithm for a class of convex optimization problems is presented. The algorithm is motivated from function approximation using a sparse combination of basis functions as well as some of its variants. We derive a bound on the rate of approximate minimization for this algorithm, and present examples of its application. Our analysis generalizes a number of earlier studies.